Enhancer detection software tools | Genome annotation

Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences.

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An integrative genomics method for the prediction of regulatory features and cis-regulatory modules in Human, Mouse, and Fly. ii-cisTarget enables: (i) to detect transcription factor motifs in a set of peaks (e.g. differentially active peaks based on H3K27ac ChIP-seq between 2 conditions) or co-expressed genes, (ii) to detect overrepresented in vivo features (histone modifications, TF ChIP-seq, DHS, Faire) for gene signatures or peaks. These regulatory features help to improve motif discovery and candidate target gene prediction, (iii) to dissect a set of co-expressed genes into direct target genes of different transcription factor motifs or ChIP-seq tracks. Some of the key features of i-cisTarget are: (i) over-represented motifs are predicted in the set of co-expressed genes, using entire intergenic and intronic sequences, (ii) 10 vertebrate species are used for motif scoring in Human and Mouse version, 12 Drosophila species are used in Drosophila version.

A tool suite designed to aid in analysis of next-generation sequencing (NGS) data. kmer-SVM uses a support vector machine (SVM) with kmer sequence features to identify predictive combinations of short transcription factor binding sites which determine the tissue specificity of the original NGS assay. Information gained from kmer-SVM can be used as an additional source of confidence in genomic experiments by recovering known binding sites, and can also reveal novel sequence features and possible cooperative mechanisms to be tested experimentally.

A discriminative computational framework to detect enhancers from DNA sequence alone that does not rely on conservation or known transcription factor binding specificities. We use a support vector machine (SVM) to differentiate enhancers from nonfunctional regions, using DNA sequence elements as features.

Allows users to deduce E-P links based on correlated activity patterns across many samples from heterogeneous sources. FOCS utilizes a cross-validation scheme in which regression models are learnt on a training set of samples. It can also evaluate on left-out samples from other cell types. This program can be applied to genomic datasets recorded by ENCODE, Roadmap Epigenomics, and FANTOM5, and on a large compendium of eRNA and gene expression profiles.

Provides a method for making sense of large in vivo regulatory datasets that do not completely align with one another and do not have celltype-specific resolution. McEnhancer is a framework that learns relevant common subsequences from an initial set of known (labeled) DHS-gene pairs, then predicts assignments for other unlabeled DHSs with similar subsequences for one expression pattern at a time.

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